Factors Associated with Outdoor Winter Walking in Older Adults: A Scoping Review
Bibliographic record
Abstract
The purpose of this study was to identify internal and external factors associated with outdoor winter walking in older adults. In this scoping review, 12 databases were searched. Inclusion criteria included English language, focus on adults 65 years of age or older, and evaluation of factors associated with outdoor winter walking. Two authors screened titles/abstracts and full text. Conflicts were resolved by consensus. Data were extracted, organized into tables, and summarized as pertaining to barriers/facilitators and internal/external factors associated with outdoor winter walking. A total of 6,843 articles were identified, 1,898 duplicates were removed, 4,789 were excluded during title/abstract screening, and 148 were excluded during full-text review. Eight studies were included. Four categories of factors affecting outdoor winter walking in older adults were identified: adverse weather conditions, physical environment, physical function, and perceptions relating to winter walking conditions. Rehabilitation and exercise professionals can use the results to educate their clients and implement the facilitators of and alternatives and solutions to barriers to outdoor winter walking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".